Physical Features Based Speech Emotion Recognition Using Predictive Classification
نویسندگان
چکیده
منابع مشابه
Physical Features Based Speech Emotion Recognition Using Predictive Classification
In the era of data explosion, speech emotion plays crucial commercial significance. Emotion recognition in speech encompasses a gamut of techniques starting from mechanical recording of audio signal to complex modeling of extracted patterns. Most challenging part of this research purview is to classify the emotion of the speech purely based on the physical characteristics of the audio signal in...
متن کاملSpeaker Emotion Recognition Based on Speech Features and Classification Techniques
Speech Processing has been developed as one of the vital provision region of Digital Signal Processing. Speaker recognition is the methodology of immediately distinguishing who is talking dependent upon special aspects held in discourse waves. This strategy makes it conceivable to utilize the speaker's voice to check their character and control access to administrations, for example voice diali...
متن کاملSpeech Emotion Recognition Using Scalogram Based Deep Structure
Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concat...
متن کاملEmotion Recognition from Speech using Discriminative Features
Creating an accurate Speech Emotion Recognition (SER) system depends on extracting features relevant to that of emotions from speech. In this paper, the features that are extracted from the speech samples include Mel Frequency Cepstral Coefficients (MFCC), energy, pitch, spectral flux, spectral roll-off and spectral stationarity. In order to avoid the 'curse of dimensionality', statis...
متن کاملSpeech emotion recognition using nonlinear dynamics features
Recent developments in man–machine interaction have motivated researchers to recognize human emotion from speech signals. In this study, we propose using nonlinear dynamics features (NLDs) for emotion recognition. NLDs are extracted from the geometrical properties of the reconstructed phase space of speech signals. The traditional prosodic and spectral features are also used as a benchmark. The...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Science and Information Technology
سال: 2016
ISSN: 0975-4660,0975-3826
DOI: 10.5121/ijcsit.2016.8205